Iterative Removal Of Features (IROF) [1]} is an extension of Deletion, where a segmentation \(S\)  of the input sample \(\mathbf {x}\)  is used.
Instead of iteratively masking the \(k\)  most important inputs, we now mask the \(k\)  most important segments.
This can reduce the number of forward passes needed, and can provide insight into the quality of an attribution at a larger scale: an algorithm that is able to find the top few pixels that maximally perturb the network when removed, might score
very well on Deletion, but not so much on IROF. If another algorithm correctly identifies the most important “regions”, it might score better on IROF and worse on Deletion. In some cases, the latter might be more interesting, as this would likely correspond to explanations that are less noisy and more easily readable.
